A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy.However,what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales,as well as its relationship with the neighborhood character-istics of customers’origins.Based on the Point of Interest(POI)measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US,our study takes Lower Manhattan,New York City,as the pilot study,and aims to examine 1)the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak,2)the areas where restaurant customers live,and 3)the association between the neighborhood characteristics of these areas and lost customers.By doing so,we provide a geographic information system-based analytical frame-work integrating the big data mining,web crawling techniques,and spatial-economic modelling.Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Integrated risk assessment framework for transformation products of emerging contaminants:what we know and what we should know

The continuous input of various emerging contaminants(ECs)has inevitably introduced large amounts of transformation products(TPs)in natural and engineering water scenarios.Structurally similar to the precursor species,the TPs are expected to possess comparative,if not more serious,environmental properties and risks.This review summarizes the state-of-the-art knowledge regarding the integrated risk assessment frameworks of TPs of ECs,mainly involving the exposure-and effectdriven analysis.The inadequate information within existing frameworks that was essential and critical for developing a better risk assessment framework was discussed.The main strategic improvements include(1)non-targeted product analysis in both laboratory and field samples,(2)omics-based highthroughput toxicity assessment,(3)multichannel-driven mode of action in conjugation with effectdirected analysis,and(4)machine learning technology.Overall,this review provides a concise but comprehensive insight into the optimized strategy for evaluating the environmental risks and screening the key toxic products from the cocktail mixtures of ECs and their TPs in the global water cycle.This facilitates deciphering the mode of toxicity in complex chemical mixtures and prioritizing the regulated TPs among the unknown products,which have the potential to be considered a class of novel"ECs"ofgreatconcern.

How does heterogeneity in dwelling type preferences relate to housing and built environment characteristics?

Understanding housing preferences is critical for successful compact city development.However,there is limited research on understanding preference heterogeneity in dwelling type choices.Using the Household Income and Labour Dynamics in Australia survey,this paper identifies the key housing and built environment characteristics associated with changes in dwelling type choice from detached houses to high-density.A latent class choice model captures the heterogeneity of dwelling type preferences within a traditionally low-density city,Brisbane,Australia.Findings reveal six household classes with distinct dwelling preferences:Class 1(senior households without children with other family members)and Class 2(couple families with children)in inner-city areas,Class 3(high-income young households)and Class 4(low-income households without children)in middle-city areas,Class 5(low-income families with children)and Class 6(middle-income young families without children)in outer-city areas.Residential environments with better access to educational facilities encourage Classes 3 and 6 to change to high-density living.Greater land use diversity encourages Classes 2,3,and 6 to move towards high-density living.Thefindings can be used to design and improve high-density housing for targeted population groups across inner-,middle-and outer-city areas.

Remote collaborative process optimization in research and design of industrial manufacturing

In response to the impact of COVID-19,the manufacturing industry and academic industrial research have largely shifted to online or hybrid conference formats.The sudden change has posed challenges for researchers and teams to adapt.Based on the current state of online conferences,inadequate communication,disruptions during meetings,confusion and loss of meeting information,and difficulties in conducting online collaborations are observed.This paper presents a design of a real-time discussion board that combines online conferences and synchronous discussions to address the issues arising from remote collaborations in industrial research.The research demonstrates that synchronous discussions conducted within multi-team industrial collaboration teams with specific and diverse issues can better control the flow of meetings,enhance meeting efficiency,promote participant interaction and engagement,reduce information loss,and weaken the boundaries between online and offline collaboration.

Human mobility data in the COVID-19 pandemic:characteristics,applications,and challenges

The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or simulate the spread of COVID-19.Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks.We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective.We identified three major sources of mobility data:public transit systems,mobile operators,and mobile phone applications.Four approaches have been commonly used to estimate human mobility:public transit-based flow,social activity patterns,index-based mobility data,and social media-derived mobility data.We compared mobility datasets’characteristics by assessing data privacy,quality,space–time coverage,high-performance data storage and processing,and accessibility.We also present challenges and future directions of using mobility data.This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

To move or stay?A cellular automata model to predict urban growth in coastal regions amidst rising sea levels

Low-lying coastal cities are widely acknowledged as the most densely populated places of urban settlement;they are also more vulnerable to risks resulting from intensive land use and land cover change,human activities,global climate change,and the rising sea levels.This study aims to predict how urban growth is affected by sea level rise(SLR)in the Australian context.We develop an urban cellular automata model incorporating urban planning policies as potential drivers or constraints of urban growth under different SLR scenarios and adaption strategies.Drawing on data capturing the socioeconomic and environmental factors in South East Queensland,Australia,our model is positioned to address one core research question:how does SLR affect future urban growth and human resettlement?Results show that urban growth in coastal regions varies depending on the extent to which the sea level rises and is affected by a combination of factors relating to urban planning and human adaptation strategies.Our study demonstrates the complexity of urban growth in coastal regions and the nuanced outcomes under different adaptation strategies in the context of rising sea levels.

A fine-grained evaluation of social vulnerability to COVID-19,healthcare access and built environmental disparities in Tokyo,Japan

Measuring vulnerability to COVID-19 and healthcare accessibility at the fine-grained level serves as the foundation for spatially explicit health planning and policy making in response to future public health crises.However,the evaluation of vulnerability and healthcare accessibility is insufficient in Japan-a nation with high population density and super-aging challenges.Drawing on the 2022 census data,transport network,medical and digital cadastral data,land use maps,and points of interest data,our study extends the concept of vulnerability in the context of COVID-19 and constructs the first fine-grained measure of vulnerability and healthcare accessibility in Tokyo Metropolis,Japan-the most populated metropolitan region in the world.We delineate the vulnerable neighbourhoods with low healthcare access and further evaluate the disparity in healthcare access and built environment of areas at different levels of vulnerability.Our outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level.Our analytical framework can be employed in different geographic contexts,guiding through place-based health planning and policy making in the post-COVID era and beyond.